Abstract To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy@5 of 83.6% for such packages that are triggered through method invocations and an accuracy@5 of 82.2% for such packages that are triggered independently.

This work was supported by the Fonds National de la Recherche (FNR), Luxembourg under projects AndroMap C13/IS/5921289 and Recommend C15/IS/10449467.

Corresponding Authors: 10.1007/s11390-017-1786-z

About author: Li Li is a research associate at Interdisciplinary Center for Security,Reliability and Trust (SnT),University of Luxembourg,Luxembourg,and a honorary research associate at the CREST group,University College London,London.